首页 | 本学科首页   官方微博 | 高级检索  
     

基于相似孤立系数的孤立点检测算法
引用本文:谢岳山,樊晓平,廖志芳,周国恩,刘世杰.基于相似孤立系数的孤立点检测算法[J].计算机工程,2013(11):200-204.
作者姓名:谢岳山  樊晓平  廖志芳  周国恩  刘世杰
作者单位:[1]中南大学信息科学与工程学院,长沙410075 [2]中南大学软件学院,长沙410075
基金项目:国家科技支撑计划基金资助项目(2012BAH08801);湖南省自然科学基金资助项目(12JJ3074)
摘    要:基于聚类的孤立点检测算法得到的结果比较粗糙,不够准确。针对该问题,提出一种基于相似孤立系数的孤立点检测算法。定义相似距离以及相似孤立点系数,给出基于相似距离的剪枝策略,根据该策略缩小可疑孤立点候选集,并降低孤立点检测算法的计算复杂度。通过选用公共数据集Iris、Labor和Segment—test进行实验验证,结果表明,该算法在发现孤立点、缩小候选集等方面相比经典孤立点检测算法更有效。

关 键 词:聚类孤立点  孤立点检测  相似孤立系数  剪枝策略  孤立点候选集

Outlier Detection Algorithm Based on Approximate Outlier Factor
XIE Yue-shana,FAN Xiao-pinga,LIAO Zhi-fangb,ZHOU Guo-enb,LIU Shi-jie.Outlier Detection Algorithm Based on Approximate Outlier Factor[J].Computer Engineering,2013(11):200-204.
Authors:XIE Yue-shana  FAN Xiao-pinga  LIAO Zhi-fangb  ZHOU Guo-enb  LIU Shi-jie
Affiliation:(a. School of Information Science and Engineering b. School of Software, Central South University, Changsha 410075, China)
Abstract:Aiming at the problem that the result of outlier detection algorithm based on clustering is coarser and not very accurate, this paper proposes an outlier detection algorithm based on Approximate Outlier Factor(AOF). This algorithm presents the definition of the similarity distance and outlier similarity coefficient, and provides a pruning strategy based on similarity distance to reduce the suspect candidate sets to decrease the computational complexity. Experiments are carried out with public datasets Iris, Labor and Segment-test, and results show that the performance of detecting outlier and reducing candidate set of this algorithm is effective compared with the classical outlier detection algorithm.
Keywords:clustering outlier  outlier detection  Approximate Outlier Factor(AOF)  pruning strategy  outlier candidate set
本文献已被 维普 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号